Overview

Dataset statistics

Number of variables58
Number of observations700
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory317.3 KiB
Average record size in memory464.2 B

Variable types

Numeric21
Categorical37

Alerts

DiabetesDuration is highly overall correlated with YearDiagnosedHigh correlation
Religion6Cat is highly overall correlated with Ethnic3CatHigh correlation
TotalEmotionalBurden is highly overall correlated with TotalPhysicianDistress and 8 other fieldsHigh correlation
TotalPhysicianDistress is highly overall correlated with TotalEmotionalBurden and 9 other fieldsHigh correlation
TotalRegimenDistress is highly overall correlated with TotalEmotionalBurden and 9 other fieldsHigh correlation
TotalInterpersonalDistress is highly overall correlated with TotalEmotionalBurden and 9 other fieldsHigh correlation
TotalDDS is highly overall correlated with TotalEmotionalBurden and 9 other fieldsHigh correlation
TotalPHQ is highly overall correlated with DepressSeverity3CatHigh correlation
YearDiagnosed is highly overall correlated with DiabetesDurationHigh correlation
Height is highly overall correlated with GenderHigh correlation
SBP is highly overall correlated with BPTarget1High correlation
LDL is highly overall correlated with TotalCHigh correlation
TotalC is highly overall correlated with LDLHigh correlation
CodeCentre is highly overall correlated with Dyslipid and 1 other fieldsHigh correlation
Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
Ethnic3Cat is highly overall correlated with Religion6CatHigh correlation
Smoke3Cat is highly overall correlated with GenderHigh correlation
SevereDDS is highly overall correlated with TotalEmotionalBurden and 9 other fieldsHigh correlation
SevereEB is highly overall correlated with TotalEmotionalBurden and 7 other fieldsHigh correlation
SeverePD is highly overall correlated with TotalPhysicianDistress and 5 other fieldsHigh correlation
SevereRD is highly overall correlated with TotalEmotionalBurden and 8 other fieldsHigh correlation
SevereIPD is highly overall correlated with TotalEmotionalBurden and 7 other fieldsHigh correlation
DistressDepress is highly overall correlated with TotalEmotionalBurden and 9 other fieldsHigh correlation
DepressSeverity3Cat is highly overall correlated with TotalPHQHigh correlation
BPTarget1 is highly overall correlated with SBPHigh correlation
HPT is highly overall correlated with AHAnumberHigh correlation
Dyslipid is highly overall correlated with CodeCentreHigh correlation
DiabetesCx1 is highly overall correlated with MicroCx1 and 2 other fieldsHigh correlation
MicroCx1 is highly overall correlated with DiabetesCx1 and 3 other fieldsHigh correlation
MacroCx1 is highly overall correlated with DiabetesCx1 and 2 other fieldsHigh correlation
Stroke is highly overall correlated with MacroCx1High correlation
IHD is highly overall correlated with DiabetesCx1 and 1 other fieldsHigh correlation
Retino is highly overall correlated with MicroCx1High correlation
Nephro is highly overall correlated with MicroCx1High correlation
DFP is highly overall correlated with MicroCx1High correlation
Diet is highly overall correlated with CodeCentreHigh correlation
OHA is highly overall correlated with BiguanideHigh correlation
Biguanide is highly overall correlated with OHAHigh correlation
AHAnumber is highly overall correlated with HPTHigh correlation
Religiosity3Cat is highly imbalanced (55.1%)Imbalance
Marital4Cat is highly imbalanced (50.5%)Imbalance
MicroCx1 is highly imbalanced (61.3%)Imbalance
MacroCx1 is highly imbalanced (67.3%)Imbalance
Stroke is highly imbalanced (85.9%)Imbalance
IHD is highly imbalanced (73.8%)Imbalance
Retino is highly imbalanced (82.0%)Imbalance
Nephro is highly imbalanced (78.5%)Imbalance
DFP is highly imbalanced (79.8%)Imbalance
OHA is highly imbalanced (56.8%)Imbalance
Biguanide is highly imbalanced (56.1%)Imbalance
AGI is highly imbalanced (89.2%)Imbalance
OHAothers is highly imbalanced (70.2%)Imbalance
APAnumber is highly imbalanced (67.0%)Imbalance
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
TotalPHQ has 105 (15.0%) zerosZeros

Reproduction

Analysis started2023-01-10 06:47:39.777500
Analysis finished2023-01-10 06:48:30.106592
Duration50.33 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.5
Minimum0
Maximum699
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:30.198760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.95
Q1174.75
median349.5
Q3524.25
95-th percentile664.05
Maximum699
Range699
Interquartile range (IQR)349.5

Descriptive statistics

Standard deviation202.21688
Coefficient of variation (CV)0.57858907
Kurtosis-1.2
Mean349.5
Median Absolute Deviation (MAD)175
Skewness0
Sum244650
Variance40891.667
MonotonicityStrictly increasing
2023-01-10T14:48:30.303370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
470 1
 
0.1%
462 1
 
0.1%
463 1
 
0.1%
464 1
 
0.1%
465 1
 
0.1%
466 1
 
0.1%
467 1
 
0.1%
468 1
 
0.1%
469 1
 
0.1%
Other values (690) 690
98.6%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
699 1
0.1%
698 1
0.1%
697 1
0.1%
696 1
0.1%
695 1
0.1%
694 1
0.1%
693 1
0.1%
692 1
0.1%
691 1
0.1%
690 1
0.1%

CodeCentre
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
3
353 
1
224 
2
123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 353
50.4%
1 224
32.0%
2 123
 
17.6%

Length

2023-01-10T14:48:30.413974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:30.504684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3 353
50.4%
1 224
32.0%
2 123
 
17.6%

Most occurring characters

ValueCountFrequency (%)
3 353
50.4%
1 224
32.0%
2 123
 
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 353
50.4%
1 224
32.0%
2 123
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 353
50.4%
1 224
32.0%
2 123
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 353
50.4%
1 224
32.0%
2 123
 
17.6%

Age
Real number (ℝ)

Distinct53
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.922857
Minimum31
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:30.588821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile39
Q150
median57
Q364
95-th percentile74
Maximum83
Range52
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.169369
Coefficient of variation (CV)0.17865177
Kurtosis-0.17450887
Mean56.922857
Median Absolute Deviation (MAD)7
Skewness-0.025707585
Sum39846
Variance103.41607
MonotonicityNot monotonic
2023-01-10T14:48:30.690561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 33
 
4.7%
57 31
 
4.4%
49 31
 
4.4%
58 31
 
4.4%
59 31
 
4.4%
55 28
 
4.0%
60 28
 
4.0%
52 27
 
3.9%
62 26
 
3.7%
50 26
 
3.7%
Other values (43) 408
58.3%
ValueCountFrequency (%)
31 3
 
0.4%
32 2
 
0.3%
33 4
0.6%
34 2
 
0.3%
35 3
 
0.4%
36 5
0.7%
37 5
0.7%
38 6
0.9%
39 9
1.3%
40 6
0.9%
ValueCountFrequency (%)
83 1
 
0.1%
82 4
0.6%
81 2
 
0.3%
80 2
 
0.3%
79 2
 
0.3%
78 2
 
0.3%
77 6
0.9%
76 7
1.0%
75 5
0.7%
74 6
0.9%

DiabetesDuration
Real number (ℝ)

Distinct31
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4671429
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:30.804935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q39
95-th percentile18
Maximum40
Range39
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.7878868
Coefficient of variation (CV)0.89496815
Kurtosis6.4066094
Mean6.4671429
Median Absolute Deviation (MAD)2
Skewness2.1600727
Sum4527
Variance33.499634
MonotonicityNot monotonic
2023-01-10T14:48:30.904562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 114
16.3%
3 85
12.1%
2 85
12.1%
1 76
10.9%
6 55
7.9%
5 45
 
6.4%
8 38
 
5.4%
9 30
 
4.3%
7 26
 
3.7%
12 23
 
3.3%
Other values (21) 123
17.6%
ValueCountFrequency (%)
1 76
10.9%
2 85
12.1%
3 85
12.1%
4 114
16.3%
5 45
 
6.4%
6 55
7.9%
7 26
 
3.7%
8 38
 
5.4%
9 30
 
4.3%
10 21
 
3.0%
ValueCountFrequency (%)
40 2
 
0.3%
35 1
 
0.1%
34 1
 
0.1%
33 2
 
0.3%
31 1
 
0.1%
28 1
 
0.1%
25 3
0.4%
24 1
 
0.1%
23 7
1.0%
22 4
0.6%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
368 
1.0
332 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 368
52.6%
1.0 332
47.4%

Length

2023-01-10T14:48:31.003980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:31.087658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 368
52.6%
1.0 332
47.4%

Most occurring characters

ValueCountFrequency (%)
0 1068
50.9%
. 700
33.3%
1 332
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1068
76.3%
1 332
 
23.7%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1068
50.9%
. 700
33.3%
1 332
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1068
50.9%
. 700
33.3%
1 332
 
15.8%

Ethnic3Cat
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
369 
3
166 
2
165 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 369
52.7%
3 166
23.7%
2 165
23.6%

Length

2023-01-10T14:48:31.154604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:31.233575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 369
52.7%
3 166
23.7%
2 165
23.6%

Most occurring characters

ValueCountFrequency (%)
1 369
52.7%
3 166
23.7%
2 165
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 369
52.7%
3 166
23.7%
2 165
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 369
52.7%
3 166
23.7%
2 165
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 369
52.7%
3 166
23.7%
2 165
23.6%

Religion6Cat
Real number (ℝ)

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8057143
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:31.295680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2265407
Coefficient of variation (CV)0.43715811
Kurtosis0.28728016
Mean2.8057143
Median Absolute Deviation (MAD)0
Skewness1.0139457
Sum1964
Variance1.5044022
MonotonicityNot monotonic
2023-01-10T14:48:31.373536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 375
53.6%
4 150
 
21.4%
3 83
 
11.9%
6 37
 
5.3%
1 33
 
4.7%
5 22
 
3.1%
ValueCountFrequency (%)
1 33
 
4.7%
2 375
53.6%
3 83
 
11.9%
4 150
 
21.4%
5 22
 
3.1%
6 37
 
5.3%
ValueCountFrequency (%)
6 37
 
5.3%
5 22
 
3.1%
4 150
 
21.4%
3 83
 
11.9%
2 375
53.6%
1 33
 
4.7%

Religiosity3Cat
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
599 
3
76 
2
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 599
85.6%
3 76
 
10.9%
2 25
 
3.6%

Length

2023-01-10T14:48:31.443724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:31.511550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 599
85.6%
3 76
 
10.9%
2 25
 
3.6%

Most occurring characters

ValueCountFrequency (%)
1 599
85.6%
3 76
 
10.9%
2 25
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 599
85.6%
3 76
 
10.9%
2 25
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 599
85.6%
3 76
 
10.9%
2 25
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 599
85.6%
3 76
 
10.9%
2 25
 
3.6%

Marital4Cat
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
555 
2
98 
4
 
26
3
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 555
79.3%
2 98
 
14.0%
4 26
 
3.7%
3 21
 
3.0%

Length

2023-01-10T14:48:31.570740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:31.640995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 555
79.3%
2 98
 
14.0%
4 26
 
3.7%
3 21
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 555
79.3%
2 98
 
14.0%
4 26
 
3.7%
3 21
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 555
79.3%
2 98
 
14.0%
4 26
 
3.7%
3 21
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 555
79.3%
2 98
 
14.0%
4 26
 
3.7%
3 21
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 555
79.3%
2 98
 
14.0%
4 26
 
3.7%
3 21
 
3.0%

Education3cat
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
580 
2
75 
0
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 580
82.9%
2 75
 
10.7%
0 45
 
6.4%

Length

2023-01-10T14:48:31.702946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:31.770379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 580
82.9%
2 75
 
10.7%
0 45
 
6.4%

Most occurring characters

ValueCountFrequency (%)
1 580
82.9%
2 75
 
10.7%
0 45
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 580
82.9%
2 75
 
10.7%
0 45
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 580
82.9%
2 75
 
10.7%
0 45
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 580
82.9%
2 75
 
10.7%
0 45
 
6.4%

Employment3Cat
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
372 
1
317 
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 372
53.1%
1 317
45.3%
2 11
 
1.6%

Length

2023-01-10T14:48:31.831164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:31.900131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 372
53.1%
1 317
45.3%
2 11
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 372
53.1%
1 317
45.3%
2 11
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 372
53.1%
1 317
45.3%
2 11
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 372
53.1%
1 317
45.3%
2 11
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 372
53.1%
1 317
45.3%
2 11
 
1.6%

Exercise
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
296 
1
235 
2
169 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
0 296
42.3%
1 235
33.6%
2 169
24.1%

Length

2023-01-10T14:48:31.971670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:32.045470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 296
42.3%
1 235
33.6%
2 169
24.1%

Most occurring characters

ValueCountFrequency (%)
0 296
42.3%
1 235
33.6%
2 169
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 296
42.3%
1 235
33.6%
2 169
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 296
42.3%
1 235
33.6%
2 169
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 296
42.3%
1 235
33.6%
2 169
24.1%

Smoke3Cat
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
534 
2
105 
1
61 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 534
76.3%
2 105
 
15.0%
1 61
 
8.7%

Length

2023-01-10T14:48:32.114500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:32.187477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 534
76.3%
2 105
 
15.0%
1 61
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 534
76.3%
2 105
 
15.0%
1 61
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 534
76.3%
2 105
 
15.0%
1 61
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 534
76.3%
2 105
 
15.0%
1 61
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 534
76.3%
2 105
 
15.0%
1 61
 
8.7%

SevereDDS
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
563 
1
137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Length

2023-01-10T14:48:32.250890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:32.314691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring characters

ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

SevereEB
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
494 
1
206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 494
70.6%
1 206
29.4%

Length

2023-01-10T14:48:32.375524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:32.441993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 494
70.6%
1 206
29.4%

Most occurring characters

ValueCountFrequency (%)
0 494
70.6%
1 206
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 494
70.6%
1 206
29.4%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 494
70.6%
1 206
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 494
70.6%
1 206
29.4%

SeverePD
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
560 
1
140 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 560
80.0%
1 140
 
20.0%

Length

2023-01-10T14:48:32.500124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:32.566013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 560
80.0%
1 140
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 560
80.0%
1 140
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 560
80.0%
1 140
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 560
80.0%
1 140
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 560
80.0%
1 140
 
20.0%

SevereRD
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
528 
1
172 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 528
75.4%
1 172
 
24.6%

Length

2023-01-10T14:48:32.618812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:32.686416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 528
75.4%
1 172
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 528
75.4%
1 172
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 528
75.4%
1 172
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 528
75.4%
1 172
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 528
75.4%
1 172
 
24.6%

SevereIPD
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
522 
1
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 522
74.6%
1 178
 
25.4%

Length

2023-01-10T14:48:32.745769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:32.833515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 522
74.6%
1 178
 
25.4%

Most occurring characters

ValueCountFrequency (%)
0 522
74.6%
1 178
 
25.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 522
74.6%
1 178
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 522
74.6%
1 178
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 522
74.6%
1 178
 
25.4%

TotalEmotionalBurden
Real number (ℝ)

Distinct25
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.872857
Minimum5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:32.891436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median11
Q315
95-th percentile22
Maximum30
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.3232672
Coefficient of variation (CV)0.44835604
Kurtosis-0.094419026
Mean11.872857
Median Absolute Deviation (MAD)4
Skewness0.69145353
Sum8311
Variance28.337174
MonotonicityNot monotonic
2023-01-10T14:48:32.966667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5 86
12.3%
11 57
 
8.1%
10 53
 
7.6%
9 51
 
7.3%
7 48
 
6.9%
8 44
 
6.3%
15 44
 
6.3%
13 42
 
6.0%
12 39
 
5.6%
6 39
 
5.6%
Other values (15) 197
28.1%
ValueCountFrequency (%)
5 86
12.3%
6 39
5.6%
7 48
6.9%
8 44
6.3%
9 51
7.3%
10 53
7.6%
11 57
8.1%
12 39
5.6%
13 42
6.0%
14 35
5.0%
ValueCountFrequency (%)
30 1
 
0.1%
28 4
 
0.6%
27 1
 
0.1%
26 1
 
0.1%
25 5
 
0.7%
24 7
1.0%
23 11
1.6%
22 17
2.4%
21 9
1.3%
20 11
1.6%

TotalPhysicianDistress
Real number (ℝ)

Distinct21
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8528571
Minimum4
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:33.032290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median6
Q310
95-th percentile18
Maximum24
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6647008
Coefficient of variation (CV)0.5940132
Kurtosis1.3361444
Mean7.8528571
Median Absolute Deviation (MAD)2
Skewness1.3850447
Sum5497
Variance21.759434
MonotonicityNot monotonic
2023-01-10T14:48:33.103867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 242
34.6%
5 72
 
10.3%
6 61
 
8.7%
8 54
 
7.7%
7 43
 
6.1%
9 36
 
5.1%
10 31
 
4.4%
12 30
 
4.3%
11 21
 
3.0%
13 18
 
2.6%
Other values (11) 92
 
13.1%
ValueCountFrequency (%)
4 242
34.6%
5 72
 
10.3%
6 61
 
8.7%
7 43
 
6.1%
8 54
 
7.7%
9 36
 
5.1%
10 31
 
4.4%
11 21
 
3.0%
12 30
 
4.3%
13 18
 
2.6%
ValueCountFrequency (%)
24 6
 
0.9%
23 3
 
0.4%
22 4
 
0.6%
21 1
 
0.1%
20 8
1.1%
19 9
1.3%
18 8
1.1%
17 9
1.3%
16 18
2.6%
15 9
1.3%

TotalRegimenDistress
Real number (ℝ)

Distinct25
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.318571
Minimum5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:33.181446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q17
median10
Q314
95-th percentile21.05
Maximum30
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2157951
Coefficient of variation (CV)0.46081744
Kurtosis0.28490236
Mean11.318571
Median Absolute Deviation (MAD)4
Skewness0.85658824
Sum7923
Variance27.204519
MonotonicityNot monotonic
2023-01-10T14:48:33.248538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5 98
14.0%
9 73
10.4%
7 52
 
7.4%
10 49
 
7.0%
13 47
 
6.7%
6 46
 
6.6%
11 44
 
6.3%
8 42
 
6.0%
12 40
 
5.7%
14 38
 
5.4%
Other values (15) 171
24.4%
ValueCountFrequency (%)
5 98
14.0%
6 46
6.6%
7 52
7.4%
8 42
6.0%
9 73
10.4%
10 49
7.0%
11 44
6.3%
12 40
5.7%
13 47
6.7%
14 38
 
5.4%
ValueCountFrequency (%)
30 1
 
0.1%
29 1
 
0.1%
28 2
 
0.3%
27 2
 
0.3%
25 8
1.1%
24 4
 
0.6%
23 9
1.3%
22 8
1.1%
21 16
2.3%
20 11
1.6%
Distinct16
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3114286
Minimum3
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:33.308970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median5
Q39
95-th percentile14
Maximum18
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6636303
Coefficient of variation (CV)0.5804756
Kurtosis0.52662814
Mean6.3114286
Median Absolute Deviation (MAD)2
Skewness1.1319037
Sum4418
Variance13.422187
MonotonicityNot monotonic
2023-01-10T14:48:33.367380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 233
33.3%
6 72
 
10.3%
4 67
 
9.6%
5 65
 
9.3%
7 59
 
8.4%
9 56
 
8.0%
12 29
 
4.1%
8 27
 
3.9%
11 19
 
2.7%
15 19
 
2.7%
Other values (6) 54
 
7.7%
ValueCountFrequency (%)
3 233
33.3%
4 67
 
9.6%
5 65
 
9.3%
6 72
 
10.3%
7 59
 
8.4%
8 27
 
3.9%
9 56
 
8.0%
10 17
 
2.4%
11 19
 
2.7%
12 29
 
4.1%
ValueCountFrequency (%)
18 6
 
0.9%
17 3
 
0.4%
16 5
 
0.7%
15 19
 
2.7%
14 11
 
1.6%
13 12
 
1.7%
12 29
4.1%
11 19
 
2.7%
10 17
 
2.4%
9 56
8.0%

TotalDDS
Real number (ℝ)

Distinct70
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.898571
Minimum17
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:34.044823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17
Q123.75
median33
Q347
95-th percentile67.05
Maximum96
Range79
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation16.077059
Coefficient of variation (CV)0.43570951
Kurtosis0.36348444
Mean36.898571
Median Absolute Deviation (MAD)11
Skewness0.90816219
Sum25829
Variance258.47181
MonotonicityNot monotonic
2023-01-10T14:48:34.166638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 45
 
6.4%
33 29
 
4.1%
23 26
 
3.7%
20 26
 
3.7%
19 26
 
3.7%
31 25
 
3.6%
35 24
 
3.4%
21 20
 
2.9%
26 20
 
2.9%
22 18
 
2.6%
Other values (60) 441
63.0%
ValueCountFrequency (%)
17 45
6.4%
18 14
 
2.0%
19 26
3.7%
20 26
3.7%
21 20
2.9%
22 18
 
2.6%
23 26
3.7%
24 14
 
2.0%
25 13
 
1.9%
26 20
2.9%
ValueCountFrequency (%)
96 2
0.3%
90 1
 
0.1%
85 2
0.3%
83 1
 
0.1%
82 4
0.6%
81 1
 
0.1%
80 1
 
0.1%
79 1
 
0.1%
78 1
 
0.1%
77 1
 
0.1%

DistressDepress
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
563 
1
137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Length

2023-01-10T14:48:34.257695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:34.337552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring characters

ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 563
80.4%
1 137
 
19.6%

TotalPHQ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6
Minimum0
Maximum26
Zeros105
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:34.400752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile13
Maximum26
Range26
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.3128712
Coefficient of variation (CV)0.93758071
Kurtosis3.343541
Mean4.6
Median Absolute Deviation (MAD)3
Skewness1.5435941
Sum3220
Variance18.600858
MonotonicityNot monotonic
2023-01-10T14:48:34.469453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 105
15.0%
1 89
12.7%
3 81
11.6%
4 68
9.7%
2 66
9.4%
5 58
8.3%
6 53
7.6%
7 38
 
5.4%
8 35
 
5.0%
9 30
 
4.3%
Other values (14) 77
11.0%
ValueCountFrequency (%)
0 105
15.0%
1 89
12.7%
2 66
9.4%
3 81
11.6%
4 68
9.7%
5 58
8.3%
6 53
7.6%
7 38
 
5.4%
8 35
 
5.0%
9 30
 
4.3%
ValueCountFrequency (%)
26 2
 
0.3%
24 1
 
0.1%
23 1
 
0.1%
22 2
 
0.3%
19 4
0.6%
18 1
 
0.1%
17 2
 
0.3%
16 7
1.0%
15 6
0.9%
14 5
0.7%
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
409 
1
215 
2
76 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 409
58.4%
1 215
30.7%
2 76
 
10.9%

Length

2023-01-10T14:48:34.537750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:34.607354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 409
58.4%
1 215
30.7%
2 76
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 409
58.4%
1 215
30.7%
2 76
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 409
58.4%
1 215
30.7%
2 76
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 409
58.4%
1 215
30.7%
2 76
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 409
58.4%
1 215
30.7%
2 76
 
10.9%

YearDiagnosed
Real number (ℝ)

Distinct31
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.5429
Minimum1973
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:34.675472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1973
5-th percentile1995
Q12004
median2009
Q32010
95-th percentile2012
Maximum2012
Range39
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.6575768
Coefficient of variation (CV)0.0028195644
Kurtosis5.5543176
Mean2006.5429
Median Absolute Deviation (MAD)2
Skewness-2.0307123
Sum1404580
Variance32.008175
MonotonicityNot monotonic
2023-01-10T14:48:34.748556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2009 113
16.1%
2010 86
12.3%
2011 84
12.0%
2012 75
10.7%
2007 54
7.7%
2008 45
 
6.4%
2005 39
 
5.6%
2004 32
 
4.6%
2006 26
 
3.7%
2001 23
 
3.3%
Other values (21) 123
17.6%
ValueCountFrequency (%)
1973 1
 
0.1%
1978 1
 
0.1%
1979 1
 
0.1%
1980 2
 
0.3%
1982 1
 
0.1%
1985 1
 
0.1%
1988 3
0.4%
1989 1
 
0.1%
1990 7
1.0%
1991 4
0.6%
ValueCountFrequency (%)
2012 75
10.7%
2011 84
12.0%
2010 86
12.3%
2009 113
16.1%
2008 45
 
6.4%
2007 54
7.7%
2006 26
 
3.7%
2005 39
 
5.6%
2004 32
 
4.6%
2003 21
 
3.0%

Weight
Real number (ℝ)

Distinct192
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.368771
Minimum40
Maximum167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:34.830463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile50.2
Q163
median70
Q380
95-th percentile100
Maximum167
Range127
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.330283
Coefficient of variation (CV)0.21183561
Kurtosis2.9540304
Mean72.368771
Median Absolute Deviation (MAD)9
Skewness1.0518337
Sum50658.14
Variance235.01758
MonotonicityNot monotonic
2023-01-10T14:48:34.915673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 25
 
3.6%
64 22
 
3.1%
71 18
 
2.6%
69 18
 
2.6%
66 18
 
2.6%
74 18
 
2.6%
67 18
 
2.6%
68 17
 
2.4%
75 17
 
2.4%
72 16
 
2.3%
Other values (182) 513
73.3%
ValueCountFrequency (%)
40 1
 
0.1%
40.8 1
 
0.1%
41.5 1
 
0.1%
42 2
0.3%
45 2
0.3%
45.4 1
 
0.1%
46 2
0.3%
47 2
0.3%
48 3
0.4%
48.3 1
 
0.1%
ValueCountFrequency (%)
167 1
0.1%
145 1
0.1%
132.5 1
0.1%
129 1
0.1%
123 1
0.1%
119 1
0.1%
116 2
0.3%
115 2
0.3%
113 1
0.1%
112 2
0.3%

Height
Real number (ℝ)

Distinct105
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.12943
Minimum100
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:35.004564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile145
Q1152
median157
Q3164
95-th percentile173
Maximum198
Range98
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9757067
Coefficient of variation (CV)0.056761773
Kurtosis2.4352845
Mean158.12943
Median Absolute Deviation (MAD)6
Skewness-0.045150074
Sum110690.6
Variance80.56331
MonotonicityNot monotonic
2023-01-10T14:48:35.094598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154 34
 
4.9%
152 31
 
4.4%
153 30
 
4.3%
162 29
 
4.1%
157 28
 
4.0%
155 28
 
4.0%
165 28
 
4.0%
158 27
 
3.9%
156 26
 
3.7%
160 25
 
3.6%
Other values (95) 414
59.1%
ValueCountFrequency (%)
100 1
 
0.1%
126 1
 
0.1%
140 2
 
0.3%
141 3
 
0.4%
142 4
 
0.6%
142.5 1
 
0.1%
143 6
0.9%
144 9
1.3%
144.5 1
 
0.1%
145 13
1.9%
ValueCountFrequency (%)
198 1
 
0.1%
184 1
 
0.1%
182.3 1
 
0.1%
181 1
 
0.1%
180 4
0.6%
179 3
0.4%
178.6 1
 
0.1%
178 1
 
0.1%
177 3
0.4%
176 2
0.3%

BPTarget1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
487 
1
213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 487
69.6%
1 213
30.4%

Length

2023-01-10T14:48:35.178114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:35.245100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 487
69.6%
1 213
30.4%

Most occurring characters

ValueCountFrequency (%)
0 487
69.6%
1 213
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 487
69.6%
1 213
30.4%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 487
69.6%
1 213
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 487
69.6%
1 213
30.4%

SBP
Real number (ℝ)

Distinct89
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.90714
Minimum83
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:35.312526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum83
5-th percentile108.95
Q1125
median136
Q3147
95-th percentile169
Maximum207
Range124
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.608725
Coefficient of variation (CV)0.12861801
Kurtosis0.43014683
Mean136.90714
Median Absolute Deviation (MAD)11
Skewness0.3266203
Sum95835
Variance310.06719
MonotonicityNot monotonic
2023-01-10T14:48:35.401847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136 26
 
3.7%
130 25
 
3.6%
140 21
 
3.0%
146 21
 
3.0%
133 20
 
2.9%
150 20
 
2.9%
144 19
 
2.7%
135 18
 
2.6%
126 18
 
2.6%
145 18
 
2.6%
Other values (79) 494
70.6%
ValueCountFrequency (%)
83 1
 
0.1%
94 1
 
0.1%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.3%
101 3
0.4%
102 3
0.4%
103 5
0.7%
105 5
0.7%
106 7
1.0%
ValueCountFrequency (%)
207 1
 
0.1%
192 2
0.3%
188 1
 
0.1%
186 1
 
0.1%
185 2
0.3%
184 1
 
0.1%
183 1
 
0.1%
182 2
0.3%
181 1
 
0.1%
180 3
0.4%

DBP
Real number (ℝ)

Distinct63
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.147857
Minimum50
Maximum162.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:35.494881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q171
median78
Q387
95-th percentile99.05
Maximum162.5
Range112.5
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.130605
Coefficient of variation (CV)0.15326511
Kurtosis3.4445021
Mean79.147857
Median Absolute Deviation (MAD)8
Skewness0.82845282
Sum55403.5
Variance147.15157
MonotonicityNot monotonic
2023-01-10T14:48:35.578638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78 45
 
6.4%
76 30
 
4.3%
90 28
 
4.0%
80 26
 
3.7%
79 25
 
3.6%
82 25
 
3.6%
73 23
 
3.3%
71 22
 
3.1%
77 21
 
3.0%
68 21
 
3.0%
Other values (53) 434
62.0%
ValueCountFrequency (%)
50 2
 
0.3%
52 1
 
0.1%
53 1
 
0.1%
54 1
 
0.1%
55 4
 
0.6%
56 7
1.0%
57 2
 
0.3%
58 1
 
0.1%
59 6
0.9%
60 13
1.9%
ValueCountFrequency (%)
162.5 1
 
0.1%
135 1
 
0.1%
123 1
 
0.1%
121 1
 
0.1%
114 1
 
0.1%
110 2
0.3%
109 2
0.3%
108 1
 
0.1%
106 4
0.6%
104 1
 
0.1%

HbA1c
Real number (ℝ)

Distinct97
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4318571
Minimum3.5
Maximum17.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:35.661306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile5.895
Q17.075
median8.1
Q39.4
95-th percentile12.6
Maximum17.1
Range13.6
Interquartile range (IQR)2.325

Descriptive statistics

Standard deviation2.0154126
Coefficient of variation (CV)0.23902357
Kurtosis1.3546581
Mean8.4318571
Median Absolute Deviation (MAD)1.2
Skewness1.0548489
Sum5902.3
Variance4.0618878
MonotonicityNot monotonic
2023-01-10T14:48:35.750587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1 92
 
13.1%
7.2 25
 
3.6%
8 20
 
2.9%
8.3 16
 
2.3%
6.8 15
 
2.1%
6.1 15
 
2.1%
6.9 15
 
2.1%
7.3 14
 
2.0%
6.6 13
 
1.9%
6 12
 
1.7%
Other values (87) 463
66.1%
ValueCountFrequency (%)
3.5 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5.1 1
 
0.1%
5.2 3
0.4%
5.3 2
 
0.3%
5.4 1
 
0.1%
5.5 4
0.6%
5.6 4
0.6%
5.7 7
1.0%
ValueCountFrequency (%)
17.1 1
0.1%
16.7 1
0.1%
16.2 1
0.1%
15.2 2
0.3%
14.9 1
0.1%
14.5 1
0.1%
14.4 1
0.1%
14.3 1
0.1%
14 2
0.3%
13.7 2
0.3%

CBG
Real number (ℝ)

Distinct154
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4065857
Minimum2.4
Maximum30.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:35.871744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile5
Q16.7
median8.7
Q311.1
95-th percentile16.4
Maximum30.5
Range28.1
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.631137
Coefficient of variation (CV)0.38602072
Kurtosis3.1616494
Mean9.4065857
Median Absolute Deviation (MAD)2.1
Skewness1.3629306
Sum6584.61
Variance13.185156
MonotonicityNot monotonic
2023-01-10T14:48:35.984456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.7 20
 
2.9%
6.3 17
 
2.4%
6.7 16
 
2.3%
7.8 14
 
2.0%
7.3 14
 
2.0%
5.7 13
 
1.9%
9.8 12
 
1.7%
6.5 11
 
1.6%
9 11
 
1.6%
6.4 11
 
1.6%
Other values (144) 561
80.1%
ValueCountFrequency (%)
2.4 1
 
0.1%
3.3 1
 
0.1%
3.5 2
0.3%
3.6 1
 
0.1%
3.7 1
 
0.1%
3.8 1
 
0.1%
3.9 1
 
0.1%
4 1
 
0.1%
4.2 3
0.4%
4.3 2
0.3%
ValueCountFrequency (%)
30.5 1
0.1%
28.6 1
0.1%
24 1
0.1%
22.3 1
0.1%
21.8 1
0.1%
21.5 1
0.1%
21.1 1
0.1%
20.9 1
0.1%
20.4 1
0.1%
19.9 1
0.1%

LDL
Real number (ℝ)

Distinct236
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9415857
Minimum0.8
Maximum7.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:36.086240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.6
Q12.4475
median2.83
Q33.345
95-th percentile4.581
Maximum7.24
Range6.44
Interquartile range (IQR)0.8975

Descriptive statistics

Standard deviation0.90013061
Coefficient of variation (CV)0.30600183
Kurtosis2.1370092
Mean2.9415857
Median Absolute Deviation (MAD)0.46
Skewness0.92921027
Sum2059.11
Variance0.81023511
MonotonicityNot monotonic
2023-01-10T14:48:36.183675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.83 137
 
19.6%
2.6 17
 
2.4%
3.8 15
 
2.1%
2.5 15
 
2.1%
3.3 14
 
2.0%
3.2 14
 
2.0%
3.1 12
 
1.7%
2.4 12
 
1.7%
2.8 11
 
1.6%
3.6 11
 
1.6%
Other values (226) 442
63.1%
ValueCountFrequency (%)
0.8 1
0.1%
0.97 1
0.1%
1.02 1
0.1%
1.06 1
0.1%
1.1 1
0.1%
1.17 1
0.1%
1.18 1
0.1%
1.2 1
0.1%
1.25 2
0.3%
1.3 2
0.3%
ValueCountFrequency (%)
7.24 1
0.1%
7.09 1
0.1%
6.74 1
0.1%
6.21 1
0.1%
6.11 1
0.1%
5.91 1
0.1%
5.9 1
0.1%
5.63 1
0.1%
5.53 1
0.1%
5.4 1
0.1%

HDL
Real number (ℝ)

Distinct110
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96484286
Minimum0.4
Maximum4.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:36.301730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.6
Q10.8
median0.91
Q31.07
95-th percentile1.44
Maximum4.03
Range3.63
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.29334361
Coefficient of variation (CV)0.30403253
Kurtosis24.863896
Mean0.96484286
Median Absolute Deviation (MAD)0.11
Skewness3.3985879
Sum675.39
Variance0.086050476
MonotonicityNot monotonic
2023-01-10T14:48:36.387941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.91 138
19.7%
0.8 54
 
7.7%
0.9 48
 
6.9%
0.7 42
 
6.0%
1 37
 
5.3%
0.6 25
 
3.6%
1.1 25
 
3.6%
1.2 12
 
1.7%
0.5 12
 
1.7%
0.78 11
 
1.6%
Other values (100) 296
42.3%
ValueCountFrequency (%)
0.4 3
 
0.4%
0.5 12
1.7%
0.55 1
 
0.1%
0.57 1
 
0.1%
0.58 1
 
0.1%
0.6 25
3.6%
0.61 1
 
0.1%
0.65 1
 
0.1%
0.66 1
 
0.1%
0.67 3
 
0.4%
ValueCountFrequency (%)
4.03 1
0.1%
3 1
0.1%
2.86 1
0.1%
2.85 1
0.1%
2.27 1
0.1%
1.97 1
0.1%
1.9 1
0.1%
1.89 1
0.1%
1.76 1
0.1%
1.69 1
0.1%

TG
Real number (ℝ)

Distinct216
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8803286
Minimum0.4
Maximum11.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:36.477624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.889
Q11.2975
median1.62
Q32.035
95-th percentile3.6085
Maximum11.3
Range10.9
Interquartile range (IQR)0.7375

Descriptive statistics

Standard deviation1.1652226
Coefficient of variation (CV)0.61969094
Kurtosis23.199359
Mean1.8803286
Median Absolute Deviation (MAD)0.37
Skewness3.9524602
Sum1316.23
Variance1.3577437
MonotonicityNot monotonic
2023-01-10T14:48:36.643843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.62 135
 
19.3%
1.2 22
 
3.1%
1.6 19
 
2.7%
1.4 18
 
2.6%
1.9 16
 
2.3%
1.7 16
 
2.3%
1 16
 
2.3%
1.1 15
 
2.1%
1.3 13
 
1.9%
0.9 12
 
1.7%
Other values (206) 418
59.7%
ValueCountFrequency (%)
0.4 2
 
0.3%
0.47 1
 
0.1%
0.54 1
 
0.1%
0.56 1
 
0.1%
0.58 1
 
0.1%
0.6 5
0.7%
0.61 1
 
0.1%
0.64 1
 
0.1%
0.65 1
 
0.1%
0.66 1
 
0.1%
ValueCountFrequency (%)
11.3 2
0.3%
11.2 1
0.1%
8.6 1
0.1%
8.4 1
0.1%
8.2 1
0.1%
7.39 1
0.1%
7.3 1
0.1%
6.76 1
0.1%
6.7 1
0.1%
5.7 1
0.1%

TotalC
Real number (ℝ)

Distinct241
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8174429
Minimum2.3
Maximum10.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-01-10T14:48:36.880176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.23
Q14.1
median4.74
Q35.27
95-th percentile6.8
Maximum10.4
Range8.1
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation1.0978227
Coefficient of variation (CV)0.22788495
Kurtosis3.0298212
Mean4.8174429
Median Absolute Deviation (MAD)0.565
Skewness1.1004449
Sum3372.21
Variance1.2052148
MonotonicityNot monotonic
2023-01-10T14:48:37.048419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.74 84
 
12.0%
5.1 19
 
2.7%
4.1 18
 
2.6%
4.2 18
 
2.6%
4.8 15
 
2.1%
4.6 13
 
1.9%
4.5 13
 
1.9%
3.7 13
 
1.9%
4.9 12
 
1.7%
4 12
 
1.7%
Other values (231) 483
69.0%
ValueCountFrequency (%)
2.3 1
0.1%
2.33 1
0.1%
2.38 1
0.1%
2.4 1
0.1%
2.6 1
0.1%
2.61 2
0.3%
2.71 1
0.1%
2.78 1
0.1%
2.8 1
0.1%
2.9 2
0.3%
ValueCountFrequency (%)
10.4 2
0.3%
9.5 1
0.1%
9.2 1
0.1%
8.95 1
0.1%
8.73 1
0.1%
8.6 1
0.1%
8.3 2
0.3%
8.25 1
0.1%
8.2 1
0.1%
8 1
0.1%

HPT
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
550 
0.0
150 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 550
78.6%
0.0 150
 
21.4%

Length

2023-01-10T14:48:37.146028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:37.222743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 550
78.6%
0.0 150
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 850
40.5%
. 700
33.3%
1 550
26.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 850
60.7%
1 550
39.3%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 850
40.5%
. 700
33.3%
1 550
26.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 850
40.5%
. 700
33.3%
1 550
26.2%

Dyslipid
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
413 
1.0
287 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 413
59.0%
1.0 287
41.0%

Length

2023-01-10T14:48:37.310155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:37.382606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 413
59.0%
1.0 287
41.0%

Most occurring characters

ValueCountFrequency (%)
0 1113
53.0%
. 700
33.3%
1 287
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1113
79.5%
1 287
 
20.5%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1113
53.0%
. 700
33.3%
1 287
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1113
53.0%
. 700
33.3%
1 287
 
13.7%

DiabetesCx1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
614 
1
86 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 614
87.7%
1 86
 
12.3%

Length

2023-01-10T14:48:37.443831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:37.514002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 614
87.7%
1 86
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 614
87.7%
1 86
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 614
87.7%
1 86
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 614
87.7%
1 86
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 614
87.7%
1 86
 
12.3%

MicroCx1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
647 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 647
92.4%
1 53
 
7.6%

Length

2023-01-10T14:48:37.575354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:37.648428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 647
92.4%
1 53
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 647
92.4%
1 53
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 647
92.4%
1 53
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 647
92.4%
1 53
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 647
92.4%
1 53
 
7.6%

MacroCx1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
658 
1
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 658
94.0%
1 42
 
6.0%

Length

2023-01-10T14:48:37.705202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:37.768417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 658
94.0%
1 42
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 658
94.0%
1 42
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 658
94.0%
1 42
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 658
94.0%
1 42
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 658
94.0%
1 42
 
6.0%

Stroke
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
686 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 686
98.0%
1.0 14
 
2.0%

Length

2023-01-10T14:48:37.823663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:37.889517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 686
98.0%
1.0 14
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 1386
66.0%
. 700
33.3%
1 14
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1386
99.0%
1 14
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1386
66.0%
. 700
33.3%
1 14
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1386
66.0%
. 700
33.3%
1 14
 
0.7%

IHD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
669 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 669
95.6%
1.0 31
 
4.4%

Length

2023-01-10T14:48:37.950555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.022863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 669
95.6%
1.0 31
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 1369
65.2%
. 700
33.3%
1 31
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1369
97.8%
1 31
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1369
65.2%
. 700
33.3%
1 31
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1369
65.2%
. 700
33.3%
1 31
 
1.5%

Retino
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
681 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 681
97.3%
1.0 19
 
2.7%

Length

2023-01-10T14:48:38.081430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.149357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 681
97.3%
1.0 19
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 1381
65.8%
. 700
33.3%
1 19
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1381
98.6%
1 19
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1381
65.8%
. 700
33.3%
1 19
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1381
65.8%
. 700
33.3%
1 19
 
0.9%

Nephro
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
676 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 676
96.6%
1.0 24
 
3.4%

Length

2023-01-10T14:48:38.204014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.269925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 676
96.6%
1.0 24
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1376
65.5%
. 700
33.3%
1 24
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1376
98.3%
1 24
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1376
65.5%
. 700
33.3%
1 24
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1376
65.5%
. 700
33.3%
1 24
 
1.1%

DFP
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
678 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 678
96.9%
1.0 22
 
3.1%

Length

2023-01-10T14:48:38.332660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.413758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 678
96.9%
1.0 22
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 1378
65.6%
. 700
33.3%
1 22
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1378
98.4%
1 22
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1378
65.6%
. 700
33.3%
1 22
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1378
65.6%
. 700
33.3%
1 22
 
1.0%

Diet
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
360 
1.0
340 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 360
51.4%
1.0 340
48.6%

Length

2023-01-10T14:48:38.480332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.548866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 360
51.4%
1.0 340
48.6%

Most occurring characters

ValueCountFrequency (%)
0 1060
50.5%
. 700
33.3%
1 340
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1060
75.7%
1 340
 
24.3%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1060
50.5%
. 700
33.3%
1 340
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1060
50.5%
. 700
33.3%
1 340
 
16.2%

OHA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
638 
0.0
 
62

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 638
91.1%
0.0 62
 
8.9%

Length

2023-01-10T14:48:38.614895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.687549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 638
91.1%
0.0 62
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 762
36.3%
. 700
33.3%
1 638
30.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 762
54.4%
1 638
45.6%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 762
36.3%
. 700
33.3%
1 638
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 762
36.3%
. 700
33.3%
1 638
30.4%

Biguanide
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
573 
0.0
126 
11.0
 
1

Length

Max length4
Median length3
Mean length3.0014286
Min length3

Characters and Unicode

Total characters2101
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 573
81.9%
0.0 126
 
18.0%
11.0 1
 
0.1%

Length

2023-01-10T14:48:38.747861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.819873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 573
81.9%
0.0 126
 
18.0%
11.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 826
39.3%
. 700
33.3%
1 575
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1401
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 826
59.0%
1 575
41.0%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 826
39.3%
. 700
33.3%
1 575
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 826
39.3%
. 700
33.3%
1 575
27.4%

Sufonylureas
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
382 
0.0
318 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 382
54.6%
0.0 318
45.4%

Length

2023-01-10T14:48:38.883571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:38.958892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 382
54.6%
0.0 318
45.4%

Most occurring characters

ValueCountFrequency (%)
0 1018
48.5%
. 700
33.3%
1 382
 
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1018
72.7%
1 382
 
27.3%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1018
48.5%
. 700
33.3%
1 382
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1018
48.5%
. 700
33.3%
1 382
 
18.2%

AGI
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
690 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 690
98.6%
1.0 10
 
1.4%

Length

2023-01-10T14:48:39.022644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:39.088694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 690
98.6%
1.0 10
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 1390
66.2%
. 700
33.3%
1 10
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1390
99.3%
1 10
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1390
66.2%
. 700
33.3%
1 10
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1390
66.2%
. 700
33.3%
1 10
 
0.5%

OHAothers
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
663 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 663
94.7%
1.0 37
 
5.3%

Length

2023-01-10T14:48:39.161156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:39.270764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 663
94.7%
1.0 37
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 1363
64.9%
. 700
33.3%
1 37
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1363
97.4%
1 37
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1363
64.9%
. 700
33.3%
1 37
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1363
64.9%
. 700
33.3%
1 37
 
1.8%

Insulin
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
427 
1.0
191 
2.0
71 
3.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 427
61.0%
1.0 191
27.3%
2.0 71
 
10.1%
3.0 11
 
1.6%

Length

2023-01-10T14:48:39.375554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:39.501500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 427
61.0%
1.0 191
27.3%
2.0 71
 
10.1%
3.0 11
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1127
53.7%
. 700
33.3%
1 191
 
9.1%
2 71
 
3.4%
3 11
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1127
80.5%
1 191
 
13.6%
2 71
 
5.1%
3 11
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1127
53.7%
. 700
33.3%
1 191
 
9.1%
2 71
 
3.4%
3 11
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1127
53.7%
. 700
33.3%
1 191
 
9.1%
2 71
 
3.4%
3 11
 
0.5%

AHAnumber
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
208 
2.0
207 
3.0
145 
0.0
82 
4.0
58 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 208
29.7%
2.0 207
29.6%
3.0 145
20.7%
0.0 82
 
11.7%
4.0 58
 
8.3%

Length

2023-01-10T14:48:39.616499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:39.758432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 208
29.7%
2.0 207
29.6%
3.0 145
20.7%
0.0 82
 
11.7%
4.0 58
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 782
37.2%
. 700
33.3%
1 208
 
9.9%
2 207
 
9.9%
3 145
 
6.9%
4 58
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 782
55.9%
1 208
 
14.9%
2 207
 
14.8%
3 145
 
10.4%
4 58
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 782
37.2%
. 700
33.3%
1 208
 
9.9%
2 207
 
9.9%
3 145
 
6.9%
4 58
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 782
37.2%
. 700
33.3%
1 208
 
9.9%
2 207
 
9.9%
3 145
 
6.9%
4 58
 
2.8%

LLAnumber
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
537 
0.0
159 
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 537
76.7%
0.0 159
 
22.7%
2.0 4
 
0.6%

Length

2023-01-10T14:48:39.924057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:40.080467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 537
76.7%
0.0 159
 
22.7%
2.0 4
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 859
40.9%
. 700
33.3%
1 537
25.6%
2 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 859
61.4%
1 537
38.4%
2 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 859
40.9%
. 700
33.3%
1 537
25.6%
2 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 859
40.9%
. 700
33.3%
1 537
25.6%
2 4
 
0.2%

APAnumber
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0.0
622 
1.0
76 
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2100
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 622
88.9%
1.0 76
 
10.9%
2.0 2
 
0.3%

Length

2023-01-10T14:48:40.188638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-10T14:48:40.309698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 622
88.9%
1.0 76
 
10.9%
2.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1322
63.0%
. 700
33.3%
1 76
 
3.6%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
66.7%
Other Punctuation 700
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1322
94.4%
1 76
 
5.4%
2 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1322
63.0%
. 700
33.3%
1 76
 
3.6%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1322
63.0%
. 700
33.3%
1 76
 
3.6%
2 2
 
0.1%

Interactions

2023-01-10T14:48:27.171311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:48.273529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:50.257345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:52.113827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:54.316677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:56.154014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:57.985334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:59.912683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:02.109578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:03.987854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:05.816531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:07.585203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:09.465505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:11.645871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:13.462584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:15.522861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:17.311694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:19.166006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:21.451688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:23.345946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:25.334534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:27.268703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:48.355487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:50.355547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-10T14:47:56.068802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:57.893944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:47:59.809800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:02.018932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:03.898033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:05.730374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:07.501653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:09.375679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:11.562562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:13.373634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:15.433260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:17.225325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:19.068308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:21.363787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:23.250899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:25.236428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-10T14:48:27.083129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-10T14:48:40.481500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0AgeDiabetesDurationReligion6CatTotalEmotionalBurdenTotalPhysicianDistressTotalRegimenDistressTotalInterpersonalDistressTotalDDSTotalPHQYearDiagnosedWeightHeightSBPDBPHbA1cCBGLDLHDLTGTotalCCodeCentreGenderEthnic3CatReligiosity3CatMarital4CatEducation3catEmployment3CatExerciseSmoke3CatSevereDDSSevereEBSeverePDSevereRDSevereIPDDistressDepressDepressSeverity3CatBPTarget1HPTDyslipidDiabetesCx1MicroCx1MacroCx1StrokeIHDRetinoNephroDFPDietOHABiguanideSufonylureasAGIOHAothersInsulinAHAnumberLLAnumberAPAnumber
Unnamed: 01.000-0.053-0.075-0.051-0.278-0.228-0.250-0.217-0.268-0.3820.0740.050-0.0250.0600.052-0.023-0.093-0.033-0.1210.002-0.0750.1460.0000.0960.0860.0000.0000.0940.0620.0000.2150.3060.1650.2880.2450.2150.2790.0000.0000.1220.1120.1300.0600.0060.0000.1120.0580.0750.0860.1110.0850.0810.0000.0820.0000.0530.0000.051
Age-0.0531.0000.2220.039-0.191-0.058-0.194-0.128-0.134-0.090-0.218-0.336-0.1960.091-0.285-0.163-0.060-0.1150.127-0.043-0.1140.0930.0140.1760.0790.2100.2440.3440.1270.1110.0890.1310.0000.1320.1020.0890.0520.0700.1820.1230.1530.0320.2010.0900.1680.0000.0580.0000.0840.0770.0000.0000.0500.1100.0000.0970.0860.199
DiabetesDuration-0.0750.2221.0000.132-0.018-0.019-0.011-0.0200.0100.059-0.989-0.1130.053-0.041-0.1970.2830.156-0.1090.102-0.000-0.0500.2590.0000.1840.1360.0000.0990.0530.1080.0000.0000.0000.0000.0400.1140.0000.0710.0000.0560.3300.2310.2620.1440.0580.1040.2840.1940.0420.1320.1380.0000.0800.1280.1060.1720.0000.0000.317
Religion6Cat-0.0510.0390.1321.0000.0720.0740.0490.0250.0920.013-0.134-0.1170.046-0.074-0.0680.0300.024-0.0790.017-0.001-0.0200.4230.0000.9420.4940.1390.1780.0770.1060.0830.1610.1460.1980.1570.1300.1610.0580.1620.0630.3110.1290.1530.0640.0480.0430.0970.0940.1690.3260.0860.1340.0000.0830.1120.0510.1000.0190.182
TotalEmotionalBurden-0.278-0.191-0.0180.0721.0000.5560.7320.6040.8280.5000.0210.0190.043-0.096-0.0290.0770.0810.0130.044-0.0230.0260.1120.0000.0470.0300.0580.0880.2020.0800.0000.6970.8920.4490.6100.5060.6970.3390.0000.0900.0970.1160.0990.0000.0000.0000.0400.0000.1660.0000.0240.0000.0000.0000.0290.0350.0000.0000.000
TotalPhysicianDistress-0.228-0.058-0.0190.0740.5561.0000.6740.6240.7760.3440.027-0.0670.036-0.163-0.1030.0050.0440.0140.140-0.1020.0240.2630.0600.1550.1920.0290.1390.0660.0000.0000.6900.5030.9370.5260.5480.6900.2380.1710.0000.2380.1040.1050.0950.0460.1140.1260.0700.1160.1390.0000.0000.0750.0680.1960.0000.0690.0000.000
TotalRegimenDistress-0.250-0.194-0.0110.0490.7320.6741.0000.7060.8700.4290.0170.0190.081-0.106-0.0030.0780.0870.0370.089-0.0320.0610.1300.0000.1550.1100.0680.0000.1550.0660.0000.7350.6370.5320.8800.6010.7350.3190.0890.1020.1410.1420.1880.0000.0310.0000.0240.1280.1890.0970.0000.0520.0000.0000.1330.0300.0580.0000.000
TotalInterpersonalDistress-0.217-0.128-0.0200.0250.6040.6240.7061.0000.7720.3580.027-0.0420.007-0.080-0.0320.0100.0580.0490.065-0.0630.0500.1780.0000.0970.0530.0000.0730.0790.0700.0300.6840.5270.5090.5650.9180.6840.2640.0850.0370.1700.0450.0360.0000.0000.0380.0000.1140.0000.0650.0000.0000.0000.0000.1210.0000.0560.0000.000
TotalDDS-0.268-0.1340.0100.0920.8280.7760.8700.7721.0000.462-0.004-0.0380.025-0.136-0.0620.0430.080-0.0010.100-0.0590.0210.1710.0000.1200.1320.0240.0000.0720.0770.0000.9150.7430.6740.7520.6980.9150.3170.0490.0000.1770.0000.0750.0780.1070.0220.0710.0000.0810.0440.0000.0450.0000.0000.1150.0000.0000.0080.000
TotalPHQ-0.382-0.0900.0590.0130.5000.3440.4290.3580.4621.000-0.056-0.0190.005-0.040-0.0300.0450.0770.0790.122-0.0100.1000.0990.1240.0550.0160.0490.0000.0240.0300.0000.4060.4460.2540.3950.3450.4060.8540.0630.0000.1340.0730.1100.0390.0000.0000.0740.0000.1800.0620.1540.0000.0000.0000.1030.0760.0390.0000.000
YearDiagnosed0.074-0.218-0.989-0.1340.0210.0270.0170.027-0.004-0.0561.0000.107-0.0520.0390.191-0.289-0.1610.107-0.098-0.0050.0430.2600.0000.1750.1070.0000.0990.0390.1050.0000.0000.0000.0000.0350.0920.0000.0560.0000.0000.3310.2280.2610.1410.0560.1020.2840.1920.0400.1270.1370.0000.0830.1270.1020.1660.0000.0000.322
Weight0.050-0.336-0.113-0.1170.019-0.0670.019-0.042-0.038-0.0190.1071.0000.4570.1060.2950.0520.0280.025-0.1830.1230.0050.1250.2760.1460.0580.1560.1460.1320.0560.1070.0000.0000.0000.0770.0780.0000.0470.0910.0000.1550.0000.0760.0000.0000.0350.0000.0000.1120.0000.0420.0000.0000.0000.0570.0000.0780.0000.000
Height-0.025-0.1960.0530.0460.0430.0360.0810.0070.0250.005-0.0520.4571.000-0.0280.0820.0330.012-0.001-0.1070.0470.0010.0000.6170.0700.0390.1460.1480.2260.0480.2480.0000.0000.0000.0960.0000.0000.0000.0770.1200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0230.0000.0000.0000.1710.0000.0830.000
SBP0.0600.091-0.041-0.074-0.096-0.163-0.106-0.080-0.136-0.0400.0390.106-0.0281.0000.457-0.0490.0160.075-0.0100.0090.0480.1550.0000.0810.0000.0000.0000.0580.0000.0000.1710.1130.1740.1670.0970.1710.0050.8210.1860.1510.0000.0000.0000.0000.0000.0000.0000.0000.1020.1550.0000.0000.0000.0470.0650.1860.0000.000
DBP0.052-0.285-0.197-0.068-0.029-0.103-0.003-0.032-0.062-0.0300.1910.2950.0820.4571.0000.0010.0000.147-0.0720.0530.1180.1680.0720.1240.0590.0510.0940.0690.1020.0240.0420.0240.0830.0180.0000.0420.0350.4910.0000.2640.0000.0390.0000.0000.0000.0000.0000.0990.0000.0000.0000.0000.0000.0000.0800.0180.0540.076
HbA1c-0.023-0.1630.2830.0300.0770.0050.0780.0100.0430.045-0.2890.0520.033-0.0490.0011.0000.4610.143-0.0440.1760.2410.1040.0000.0450.0000.0000.0000.0000.0000.0820.0000.0460.0000.0000.0000.0000.0200.0680.1080.0000.0000.0710.0000.0000.0000.0970.0810.0430.1530.0930.0000.1380.0380.0790.2810.0400.0000.000
CBG-0.093-0.0600.1560.0240.0810.0440.0870.0580.0800.077-0.1610.0280.0120.0160.0000.4611.0000.085-0.0140.1360.1730.0730.0000.0470.0000.0000.0880.0000.0000.0000.0030.0000.0000.0000.0000.0030.0220.0000.1380.0760.0340.0200.0490.0000.0840.0000.0000.0000.0000.0220.0000.0780.0000.0000.2250.0590.0570.000
LDL-0.033-0.115-0.109-0.0790.0130.0140.0370.049-0.0010.0790.1070.025-0.0010.0750.1470.1430.0851.0000.213-0.0340.8280.1480.0560.0570.0760.0300.0000.0000.0500.0990.0000.0000.0000.0370.0320.0000.0870.0380.1160.1800.0840.1100.0000.0000.0000.0810.0890.0000.0000.0440.0640.1140.0000.0880.1210.0680.1280.000
HDL-0.1210.1270.1020.0170.0440.1400.0890.0650.1000.122-0.098-0.183-0.107-0.010-0.072-0.044-0.0140.2131.000-0.3220.2990.3270.1970.1650.2070.1540.1080.0700.0880.0260.0880.0000.0630.0450.0540.0880.0470.0000.0960.2540.1320.1360.0640.0000.1000.2370.1820.0000.2860.0510.0000.0150.0000.1820.0310.0000.0000.000
TG0.002-0.043-0.000-0.001-0.023-0.102-0.032-0.063-0.059-0.010-0.0050.1230.0470.0090.0530.1760.136-0.034-0.3221.0000.2120.0970.1490.0460.0000.0780.0650.0480.0000.0000.0770.0820.0620.0680.1010.0770.0000.0500.0100.0000.0000.0720.0390.0000.0000.0000.0960.0960.0610.1170.0000.0000.0430.0840.1030.0640.0000.000
TotalC-0.075-0.114-0.050-0.0200.0260.0240.0610.0500.0210.1000.0430.0050.0010.0480.1180.2410.1730.8280.2990.2121.0000.0960.0260.0000.0000.0000.0000.0000.0740.0870.0000.0000.0000.0000.0000.0000.0650.0000.0000.1000.1000.0900.0580.0000.0420.0000.0320.0000.1360.1190.0970.0630.0000.1420.1160.0000.0910.000
CodeCentre0.1460.0930.2590.4230.1120.2630.1300.1780.1710.0990.2600.1250.0000.1550.1680.1040.0730.1480.3270.0970.0961.0000.0690.4020.3250.1230.1440.0390.1770.0950.2250.1160.2870.1490.1970.2250.0410.1830.2260.7760.3030.3340.0710.0260.1130.1530.2580.2560.7160.0980.0560.0000.0470.3270.1360.1090.1420.188
Gender0.0000.0140.0000.0000.0000.0600.0000.0000.0000.1240.0000.2760.6170.0000.0720.0000.0000.0560.1970.1490.0260.0691.0000.0000.0240.3170.1780.3430.0000.5440.0000.0000.0000.0000.0000.0000.0140.0000.0350.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.1400.0000.0000.0000.0000.0450.0790.033
Ethnic3Cat0.0960.1760.1840.9420.0470.1550.1550.0970.1200.0550.1750.1460.0700.0810.1240.0450.0470.0570.1650.0460.0000.4020.0001.0000.4230.1580.1590.0870.1130.0710.1300.0860.2010.1270.0980.1300.0530.1340.0960.3210.1170.1430.0740.0390.0420.0870.0910.1610.3000.0000.0000.0000.0890.0660.0780.1280.0220.164
Religiosity3Cat0.0860.0790.1360.4940.0300.1920.1100.0530.1320.0160.1070.0580.0390.0000.0590.0000.0000.0760.2070.0000.0000.3250.0240.4231.0000.1230.0670.0000.0330.0830.1060.0400.1940.1400.0830.1060.0450.0430.0410.1880.0080.0000.0660.0000.0530.0330.0000.0000.2820.0560.0000.0000.0000.0920.0610.0900.0000.122
Marital4Cat0.0000.2100.0000.1390.0580.0290.0680.0000.0240.0490.0000.1560.1460.0000.0510.0000.0000.0300.1540.0780.0000.1230.3170.1580.1231.0000.1490.2030.0720.1070.0000.0510.0140.0730.0350.0000.0000.0000.0430.0000.0710.0320.0000.0000.0340.0000.0180.0460.1660.0000.0000.0000.0000.0630.0250.0170.0000.000
Education3cat0.0000.2440.0990.1780.0880.1390.0000.0730.0000.0000.0990.1460.1480.0000.0940.0000.0880.0000.1080.0650.0000.1440.1780.1590.0670.1491.0000.1440.0870.0450.0000.0160.0890.0320.0120.0000.0000.0000.0420.0970.0460.0310.0490.0370.0510.0350.0580.0380.0530.0750.0000.0000.0000.0000.0520.0000.0140.079
Employment3Cat0.0940.3440.0530.0770.2020.0660.1550.0790.0720.0240.0390.1320.2260.0580.0690.0000.0000.0000.0700.0480.0000.0390.3430.0870.0000.2030.1441.0000.0410.1220.0780.0990.0970.0790.0730.0780.0830.0200.1300.0140.0000.0000.0660.0710.0380.0000.0000.0000.0300.0000.0000.0000.0000.0470.0000.0760.0400.000
Exercise0.0620.1270.1080.1060.0800.0000.0660.0700.0770.0300.1050.0560.0480.0000.1020.0000.0000.0500.0880.0000.0740.1770.0000.1130.0330.0720.0870.0411.0000.0220.0000.0670.0000.0000.0000.0000.0380.0500.1150.1740.1030.0390.0800.0000.0920.0740.0000.0570.1120.0000.0000.0440.0000.1250.0000.1010.0160.096
Smoke3Cat0.0000.1110.0000.0830.0000.0000.0000.0300.0000.0000.0000.1070.2480.0000.0240.0820.0000.0990.0260.0000.0870.0950.5440.0710.0830.1070.0450.1220.0221.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0350.0090.0000.0000.0500.0000.0000.0000.0000.0480.0000.0230.0590.0790.0000.0000.0780.0000.059
SevereDDS0.2150.0890.0000.1610.6970.6900.7350.6840.9150.4060.0000.0000.0000.1710.0420.0000.0030.0000.0880.0770.0000.2250.0000.1300.1060.0000.0000.0780.0000.0001.0000.6570.6570.6920.6420.9950.3560.0840.0000.1740.0140.0420.0000.0000.0000.0110.0410.0000.0440.0000.0930.0000.0000.0930.0000.0990.0000.000
SevereEB0.3060.1310.0000.1460.8920.5030.6370.5270.7430.4460.0000.0000.0000.1130.0240.0460.0000.0000.0000.0820.0000.1160.0000.0860.0400.0510.0160.0990.0670.0000.6571.0000.4160.6030.4750.6570.4150.0000.0000.1020.0000.0440.0000.0000.0000.0410.0460.0000.0000.0000.0550.0000.0000.0000.0000.1000.0000.000
SeverePD0.1650.0000.0000.1980.4490.9370.5320.5090.6740.2540.0000.0000.0000.1740.0830.0000.0000.0000.0630.0620.0000.2870.0000.2010.1940.0140.0890.0970.0000.0000.6570.4161.0000.4810.4740.6570.2470.1580.0000.1710.0000.0000.0000.0000.0000.0000.0000.0000.0730.0490.0190.0000.0000.1400.0370.1400.0000.062
SevereRD0.2880.1320.0400.1570.6100.5260.8800.5650.7520.3950.0350.0770.0960.1670.0180.0000.0000.0370.0450.0680.0000.1490.0000.1270.1400.0730.0320.0790.0000.0000.6920.6030.4811.0000.5460.6920.3600.0450.0000.1290.0640.0720.0000.0310.0000.0000.0750.0120.0130.0000.0660.0000.0000.0340.0000.0920.0000.000
SevereIPD0.2450.1020.1140.1300.5060.5480.6010.9180.6980.3450.0920.0780.0000.0970.0000.0000.0000.0320.0540.1010.0000.1970.0000.0980.0830.0350.0120.0730.0000.0000.6420.4750.4740.5461.0000.6420.3040.0000.0000.1590.0270.0640.0000.0000.0000.0000.0210.0630.0450.0000.0000.0000.0000.0250.0000.0870.0000.000
DistressDepress0.2150.0890.0000.1610.6970.6900.7350.6840.9150.4060.0000.0000.0000.1710.0420.0000.0030.0000.0880.0770.0000.2250.0000.1300.1060.0000.0000.0780.0000.0000.9950.6570.6570.6920.6421.0000.3560.0840.0000.1740.0140.0420.0000.0000.0000.0110.0410.0000.0440.0000.0930.0000.0000.0930.0000.0990.0000.000
DepressSeverity3Cat0.2790.0520.0710.0580.3390.2380.3190.2640.3170.8540.0560.0470.0000.0050.0350.0200.0220.0870.0470.0000.0650.0410.0140.0530.0450.0000.0000.0830.0380.0000.3560.4150.2470.3600.3040.3561.0000.0000.0000.0460.0200.0210.0400.0270.0000.0680.0000.0000.0180.0220.0000.0000.0570.0000.0320.0680.0230.000
BPTarget10.0000.0700.0000.1620.0000.1710.0890.0850.0490.0630.0000.0910.0770.8210.4910.0680.0000.0380.0000.0500.0000.1830.0000.1340.0430.0000.0000.0200.0500.0000.0840.0000.1580.0450.0000.0840.0001.0000.1140.1350.0000.0000.0000.0000.0000.0000.0000.0330.0000.0290.0000.0250.0000.0000.0700.2080.0620.034
HPT0.0000.1820.0560.0630.0900.0000.1020.0370.0000.0000.0000.0000.1200.1860.0000.1080.1380.1160.0960.0100.0000.2260.0350.0960.0410.0430.0420.1300.1150.0380.0000.0000.0000.0000.0000.0000.0000.1141.0000.2160.0750.0340.0710.0000.0590.0000.0340.0000.0000.0000.0000.0470.0000.0000.0560.6430.1000.118
Dyslipid0.1220.1230.3300.3110.0970.2380.1410.1700.1770.1340.3310.1550.0000.1510.2640.0000.0760.1800.2540.0000.1000.7760.0330.3210.1880.0000.0970.0140.1740.0350.1740.1020.1710.1290.1590.1740.0460.1350.2161.0000.1930.2140.0810.0000.0560.1330.1660.1010.0970.0000.0660.0000.0000.0870.1030.1420.4150.245
DiabetesCx10.1120.1530.2310.1290.1160.1040.1420.0450.0000.0730.2280.0000.0000.0000.0000.0000.0340.0840.1320.0000.1000.3030.0000.1170.0080.0710.0460.0000.1030.0090.0140.0000.0000.0640.0270.0140.0200.0000.0750.1931.0000.7560.6650.3640.5640.4320.4900.4680.1410.0980.0350.0000.0000.0690.1230.0630.0500.256
MicroCx10.1300.0320.2620.1530.0990.1050.1880.0360.0750.1100.2610.0760.0000.0000.0390.0710.0200.1100.1360.0720.0900.3340.0000.1430.0000.0320.0310.0000.0390.0000.0420.0440.0000.0720.0640.0420.0210.0000.0340.2140.7561.0000.1150.0410.1020.5660.6430.6130.1440.1040.0000.0590.0000.0410.1430.0000.0440.152
MacroCx10.0600.2010.1440.0640.0000.0950.0000.0000.0780.0390.1410.0000.0000.0000.0000.0000.0490.0000.0640.0390.0580.0710.0000.0740.0660.0000.0490.0660.0800.0000.0000.0000.0000.0000.0000.0000.0400.0000.0710.0810.6650.1151.0000.5430.8370.1570.0940.0000.0000.0000.0110.0000.0000.0270.0650.0380.0440.333
Stroke0.0060.0900.0580.0480.0000.0460.0310.0000.1070.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0390.0000.0000.0370.0710.0000.0500.0000.0000.0000.0310.0000.0000.0270.0000.0000.0000.3640.0410.5431.0000.0850.0590.0430.0000.0280.0000.0760.0220.0000.0000.0000.0000.0110.101
IHD0.0000.1680.1040.0430.0000.1140.0000.0380.0220.0000.1020.0350.0000.0000.0000.0000.0840.0000.1000.0000.0420.1130.0000.0420.0530.0340.0510.0380.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0560.5640.1020.8370.0851.0000.1070.0850.0000.0660.0200.0000.0000.0000.0000.0620.0520.0560.323
Retino0.1120.0000.2840.0970.0400.1260.0240.0000.0710.0740.2840.0000.0000.0000.0000.0970.0000.0810.2370.0000.0000.1530.0000.0870.0330.0000.0350.0000.0740.0000.0110.0410.0000.0000.0000.0110.0680.0000.0000.1330.4320.5660.1570.0590.1071.0000.2310.0250.0130.0790.0000.0770.0000.0000.1540.0000.0750.159
Nephro0.0580.0580.1940.0940.0000.0700.1280.1140.0000.0000.1920.0000.0000.0000.0000.0810.0000.0890.1820.0960.0320.2580.0000.0910.0000.0180.0580.0000.0000.0000.0410.0460.0000.0750.0210.0410.0000.0000.0340.1660.4900.6430.0940.0430.0850.2311.0000.1180.1170.0540.0130.0620.0000.0000.1490.0590.0660.098
DFP0.0750.0000.0420.1690.1660.1160.1890.0000.0810.1800.0400.1120.0000.0000.0990.0430.0000.0000.0000.0960.0000.2560.0000.1610.0000.0460.0380.0000.0570.0000.0000.0000.0000.0120.0630.0000.0000.0330.0000.1010.4680.6130.0000.0000.0000.0250.1181.0000.1220.0000.0000.0000.0000.0000.0830.0000.0000.000
Diet0.0860.0840.1320.3260.0000.1390.0970.0650.0440.0620.1270.0000.0660.1020.0000.1530.0000.0000.2860.0610.1360.7160.0000.3000.2820.1660.0530.0300.1120.0480.0440.0000.0730.0130.0450.0440.0180.0000.0000.0970.1410.1440.0000.0280.0660.0130.1170.1221.0000.0000.0750.0000.0000.2200.1170.1200.0320.138
OHA0.1110.0770.1380.0860.0240.0000.0000.0000.0000.1540.1370.0420.0000.1550.0000.0930.0220.0440.0510.1170.1190.0980.0940.0000.0560.0000.0750.0000.0000.0000.0000.0000.0490.0000.0000.0000.0220.0290.0000.0000.0980.1040.0000.0000.0200.0790.0540.0000.0001.0000.6640.3350.0000.0500.3870.0920.0000.000
Biguanide0.0850.0000.0000.1340.0000.0000.0520.0000.0450.0000.0000.0000.0230.0000.0000.0000.0000.0640.0000.0000.0970.0560.1400.0000.0000.0000.0000.0000.0000.0230.0930.0550.0190.0660.0000.0930.0000.0000.0000.0660.0350.0000.0110.0760.0000.0000.0130.0000.0750.6641.0000.2640.0190.4350.1820.0000.0190.000
Sufonylureas0.0810.0000.0800.0000.0000.0750.0000.0000.0000.0000.0830.0000.0000.0000.0000.1380.0780.1140.0150.0000.0630.0000.0000.0000.0000.0000.0000.0000.0440.0590.0000.0000.0000.0000.0000.0000.0000.0250.0470.0000.0000.0590.0000.0220.0000.0770.0620.0000.0000.3350.2641.0000.0630.1850.3810.0650.0000.022
AGI0.0000.0500.1280.0830.0000.0680.0000.0000.0000.0000.1270.0000.0000.0000.0000.0380.0000.0000.0000.0430.0000.0470.0000.0890.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0631.0000.0000.0320.0000.0000.216
OHAothers0.0820.1100.1060.1120.0290.1960.1330.1210.1150.1030.1020.0570.0000.0470.0000.0790.0000.0880.1820.0840.1420.3270.0000.0660.0920.0630.0000.0470.1250.0000.0930.0000.1400.0340.0250.0930.0000.0000.0000.0870.0690.0410.0270.0000.0000.0000.0000.0000.2200.0500.4350.1850.0001.0000.1160.0520.0150.000
Insulin0.0000.0000.1720.0510.0350.0000.0300.0000.0000.0760.1660.0000.1710.0650.0800.2810.2250.1210.0310.1030.1160.1360.0000.0780.0610.0250.0520.0000.0000.0000.0000.0000.0370.0000.0000.0000.0320.0700.0560.1030.1230.1430.0650.0000.0620.1540.1490.0830.1170.3870.1820.3810.0320.1161.0000.0790.0500.000
AHAnumber0.0530.0970.0000.1000.0000.0690.0580.0560.0000.0390.0000.0780.0000.1860.0180.0400.0590.0680.0000.0640.0000.1090.0450.1280.0900.0170.0000.0760.1010.0780.0990.1000.1400.0920.0870.0990.0680.2080.6430.1420.0630.0000.0380.0000.0520.0000.0590.0000.1200.0920.0000.0650.0000.0520.0791.0000.0440.000
LLAnumber0.0000.0860.0000.0190.0000.0000.0000.0000.0080.0000.0000.0000.0830.0000.0540.0000.0570.1280.0000.0000.0910.1420.0790.0220.0000.0000.0140.0400.0160.0000.0000.0000.0000.0000.0000.0000.0230.0620.1000.4150.0500.0440.0440.0110.0560.0750.0660.0000.0320.0000.0190.0000.0000.0150.0500.0441.0000.000
APAnumber0.0510.1990.3170.1820.0000.0000.0000.0000.0000.0000.3220.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.1880.0330.1640.1220.0000.0790.0000.0960.0590.0000.0000.0620.0000.0000.0000.0000.0340.1180.2450.2560.1520.3330.1010.3230.1590.0980.0000.1380.0000.0000.0220.2160.0000.0000.0000.0001.000

Missing values

2023-01-10T14:48:29.305266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-10T14:48:29.683762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0CodeCentreAgeDiabetesDurationGenderEthnic3CatReligion6CatReligiosity3CatMarital4CatEducation3catEmployment3CatExerciseSmoke3CatSevereDDSSevereEBSeverePDSevereRDSevereIPDTotalEmotionalBurdenTotalPhysicianDistressTotalRegimenDistressTotalInterpersonalDistressTotalDDSDistressDepressTotalPHQDepressSeverity3CatYearDiagnosedWeightHeightBPTarget1SBPDBPHbA1cCBGLDLHDLTGTotalCHPTDyslipidDiabetesCx1MicroCx1MacroCx1StrokeIHDRetinoNephroDFPDietOHABiguanideSufonylureasAGIOHAothersInsulinAHAnumberLLAnumberAPAnumber
00168.051.03411112000000545317030200876.0162.00137.066.08.13.32.830.911.624.741.00.01100.00.00.00.01.00.01.01.00.00.00.02.02.00.01.0
11165.0331.02311110000000847322020198076.5155.50147.081.06.84.81.850.923.044.151.01.00000.00.00.00.00.00.00.00.00.00.00.01.02.01.01.0
22156.090.01212102000100121614626020200462.6159.0199.060.07.98.12.471.191.164.190.01.00000.00.00.00.00.00.01.01.01.00.00.00.00.01.00.0
33161.050.021211020000017111212420102200865.0163.01127.075.09.610.71.911.440.613.631.01.00000.00.00.00.00.00.01.01.00.00.00.01.01.01.00.0
44158.0200.012111010010111511159500122199357.0142.51115.078.08.116.13.181.221.144.921.01.00000.00.00.00.00.00.01.01.00.00.00.01.01.01.00.0
55158.081.03411110000100813113350102200589.0168.00136.096.08.114.02.230.717.396.301.00.00000.00.00.00.00.00.01.00.00.00.01.01.04.01.00.0
66354.040.0121110101111128202212821132200974.0152.00136.078.09.113.83.600.601.204.701.00.01010.01.00.00.00.00.01.01.01.00.00.00.02.00.01.0
77167.0150.0231310001100122111316621262199841.5145.31120.070.012.515.22.710.984.495.731.01.00000.00.00.00.00.00.00.00.00.00.00.01.03.01.00.0
88139.031.02634111000100121313846061201058.8162.21121.072.07.710.32.531.491.104.520.01.00000.00.00.00.00.00.01.00.00.00.01.00.01.01.00.0
99252.020.01211101001000249127270132201170.0155.00150.083.06.16.36.741.142.358.951.01.00000.00.00.00.00.01.01.00.01.00.00.00.02.01.00.0
Unnamed: 0CodeCentreAgeDiabetesDurationGenderEthnic3CatReligion6CatReligiosity3CatMarital4CatEducation3catEmployment3CatExerciseSmoke3CatSevereDDSSevereEBSeverePDSevereRDSevereIPDTotalEmotionalBurdenTotalPhysicianDistressTotalRegimenDistressTotalInterpersonalDistressTotalDDSDistressDepressTotalPHQDepressSeverity3CatYearDiagnosedWeightHeightBPTarget1SBPDBPHbA1cCBGLDLHDLTGTotalCHPTDyslipidDiabetesCx1MicroCx1MacroCx1StrokeIHDRetinoNephroDFPDietOHABiguanideSufonylureasAGIOHAothersInsulinAHAnumberLLAnumberAPAnumber
690690250.081.03411111000000545317000200588.0166.01102.073.011.59.73.010.783.345.311.01.00000.00.00.00.00.01.01.01.00.00.00.01.02.01.01.0
691691247.080.012111120000007711429040200550.0146.00146.071.011.28.42.920.761.234.241.01.00000.00.00.00.00.01.01.01.00.00.00.01.01.01.00.0
692692361.040.0341210000000014611637000200979.0160.00154.096.05.84.72.800.701.404.101.00.00000.00.00.00.00.01.01.00.01.00.00.00.02.01.00.0
693693156.040.0121111201001158211852100200968.4154.00133.071.08.06.93.311.221.365.151.01.00000.00.00.00.00.00.01.00.00.00.01.00.02.01.00.0
694694355.010.03511210000000948324010201240.0146.01129.070.06.27.02.000.901.403.500.00.00000.00.00.00.00.01.01.01.00.00.00.00.00.01.00.0
695695363.050.0121110200000010611835000200862.5145.01128.070.08.09.02.830.911.624.741.00.00000.00.00.00.00.01.01.01.01.00.00.01.01.01.00.0
696696260.050.0121111000100016912845000200854.0152.00158.083.06.810.33.250.872.035.041.01.00000.00.00.00.00.01.01.01.01.00.00.00.02.01.00.0
697697155.081.01211212000001127141043010200576.6160.00157.078.08.410.83.911.211.645.781.01.00000.00.00.00.00.00.01.01.01.00.00.01.04.00.00.0
698698348.010.0121110101111123172314771162201263.0149.00160.061.08.18.14.500.801.506.101.00.00000.00.00.00.00.01.01.01.01.00.00.01.03.01.00.0
699699363.041.01211101100000645318000200979.0166.00166.067.05.29.12.830.911.624.301.00.00000.00.00.00.00.01.01.01.01.00.00.00.03.00.00.0